Describing Pulmonary Nodules Using 3D Clustering

Journal article


Al-Funjan, A., Farid Meziane and Aspin, R. 2022. Describing Pulmonary Nodules Using 3D Clustering. Advanced Engineering Research. 22 (3), pp. 261-271. https://doi.org/10.23947/2687-1653-2022-22-3-261-271
AuthorsAl-Funjan, A., Farid Meziane and Aspin, R.
Abstract

After detecting a node (tumor) on medical images, it is necessary to determine its shape, localization and type. This is important for the choice of the type of clinical intervention and other aspects of the work of radiologists. Computed detection systems effectively locate nodes using 2D computed tomography (CT) imaging of the lungs. However, a more detailed description of the node (tumor) is still a big problem. In the framework of this work, three-dimensional clustering was performed on volumetric CT images, which give an idea of ​​the node and its structure. These materials were used to describe the development of the node in successive sections of the lung. Combined algorithms for clustering and determining the characteristics of nodes in 3D visualization. Some 3D features were applied to objects grouped by K-means CT lung imaging. This approach provides a visual study of the three-dimensional shape and location of the node. This study is mainly focused on clustering in 3D in order to obtain complex information missing from the radiologist's report. In addition, to evaluate the proposed system, we used a 3D density clustering algorithm for spatial data with the presence of noise and another 3D application - a graph. The proposed method detected a difficult case and automatically provided information about the types of nodes (globular, juxtapleural, and pleural-caudal). The algorithm is tested on standard data, Based on the proposed model, it is possible to cluster lung nodes in 3D CT and determine a set of characteristics such as shape, location, and type.

Keywordsautomated 3D clustering ; lung CT ; description of node characteristics
Year2022
JournalAdvanced Engineering Research
Journal citation22 (3), pp. 261-271
PublisherDon State Technical University
ISSN2687-1653
Digital Object Identifier (DOI)https://doi.org/10.23947/2687-1653-2022-22-3-261-271
Web address (URL)https://doi.org/10.23947/2687-1653-2022-22-3-261-271
Output statusPublished
Publication dates13 Oct 2022
Publication process dates
Accepted30 Aug 2022
Deposited15 Nov 2022
Permalink -

https://repository.derby.ac.uk/item/9v35y/describing-pulmonary-nodules-using-3d-clustering

  • 12
    total views
  • 0
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

A review of the generation of requirements specification in natural language using objects UML models and domain ontology
Abdalazeima, Alaa and Meziane, Farid 2021. A review of the generation of requirements specification in natural language using objects UML models and domain ontology. Procedia Computer Science. 189, pp. 328-334. https://doi.org/10.1016/j.procs.2021.05.102
Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach
Swee, C.P., Labadin, J. and Meziane, F. 2022. Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach. Journal of Computing and Social Informatics. 1 (2), pp. 1-16. https://doi.org/10.33736/jcsi.4761.2022
DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications
Mir, F. and Meziane, F. 2022. DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications. Cluster Computing. pp. 1-22. https://doi.org/10.1007/s10586-022-03741-w
Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm
Almomani, Ammar, Nawasrah, Ahmad Al, Alauthman, Mohammad, Betar, Mohammed Azmi Al and Meziane, Farid 2021. Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm. International Journal of Ad Hoc and Ubiquitous Computing. 36 (1), p. 50. https://doi.org/10.1016/j.cosrev.2020.100305
MRI brain classification using the quantum entropy LBP and deep-learning-based features
Hasan, Ali M., Jalab, Hamid A., Ibrahim, Rabha W., Meziane, Farid, AL-Shamasneh, Ala’a R. and Obaiys, Suzan J. 2020. MRI brain classification using the quantum entropy LBP and deep-learning-based features. Entropy. 22 (9), p. 1033. https://doi.org/10.3390/e22091033
Arabic machine translation: A survey of the latest trends and challenges
Ameur, M.S.H., Meziane, Farid and Guessoum, Ahmed 2020. Arabic machine translation: A survey of the latest trends and challenges. Computer Science Review. 38, p. 100305. https://doi.org/10.1016/j.cosrev.2020.100305